Structural Reliability Algorithms of Kriging Model Based on Improved Learning Strategy
| Autor: | Huacong Li, Hongliang Xiao, Kai Peng, Linxiong Hong |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2020 |
| Předmět: |
monte carlo method
Active learning (machine learning) Computer science Monte Carlo method 0211 other engineering and technologies 020101 civil engineering 02 engineering and technology 0201 civil engineering Kriging Local search (optimization) Limit state design Reliability (statistics) Motor vehicles. Aeronautics. Astronautics 021110 strategic defence & security studies algorithm business.industry kriging model General Engineering TL1-4050 active learning function structural reliability Nonlinear system failure probability business Focus (optics) Algorithm |
| Zdroj: | Xibei Gongye Daxue Xuebao, Vol 38, Iss 2, Pp 412-419 (2020) |
| ISSN: | 2609-7125 1000-2758 |
| Popis: | Aiming at the problems of implicit and highly nonlinear limit state function in the process of reliability analysis of mechanical products, a reliability analysis method of mechanical structures based on Kriging model and improved EGO active learning strategy is proposed. For the problem that the traditional EGO method cannot effectively select points in the limit state surface region, an improved EGO method is proposed. By dealing with the predicted values of sample point model with absolute values and assume that the distribution state of response values remains the same, the work focus of active learning selection points is moved to the vicinity, where the points are with larger prediction variance or close to the limit state surface. Three examples show that, compared with the classical active learning method, the proposed method has good global and local search ability, and can estimate the exact failure probability value under the condition of less calculation of the limit state function. |
| Databáze: | OpenAIRE |
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